AI will not show the “whole story” if your data is missing chapters

Data is the lifeblood of AI. An AI system needs to learn from data, as well as from humans, in order to be able to fulfill its function. Unfortunately, organizations are already struggling to integrate data from multiple sources to create a single source of truth on their customers, products, or other data. AI will not solve these data issues, it will only make them more pronounced. In this blog post, I will talk about how Qlik’s approach to AI combined with its unique Associative Indexing Engine and Qlik Data Catalyst product handles this problem.

Accessing and associating all of the data will be the key enabler as Artificial Intelligence comes of age. There are vast amounts of enterprise data in various organizational silos as well as public domain data sources. To enable a holistic view of a complex problem, making connections between these data sets is critical, from which new AI-driven insights can be identified. Essentially, if the analytics technology does not allow users getting the full story from their data, building AI around it will only make the problem more evident.

It’s also important to note that without access to the complete enterprise data schema, and without indexing and starting with all known associations across the data values, the machine learning capabilities would naturally be throttled. It would be like having a V8 engine and only supplying fuel to half of the cylinders. With Qlik, the system easily combines all data sources, no matter how many, or how large. Qlik’s Associative Engine indexes all data relationships with no data left behind. This powerful one-of-a kind associative engine enables Qlik Cognitive Engine, Qlik’s AI framework, to learn from all of the data, without missing any chapters.

Thus, Qlik’s unique advantage is the combination of its Associative Technology, combined with the Qlik Cognitive Engine enables users to gain insights based on the “whole story”. The combination of associative indexing with augmented intelligence helps users quickly seek out and surface the useful, interesting and relevant insights across all data that they may not even know to look for. In essence, Qlik delivers the power of AI2 (Associative Indexing * Augmented Intelligence).

With vast amounts of data coming from multiple disparate systems, an effective data governance strategy also becomes important for AI to produce trustworthy insights. Data governance offers a simple and direct way to ensure that right data is used to generate insights, but also identifies data errors and quickly flags and resolves those errors to help maintain the organization’s confidence on data and ultimately on the insights generated. To take this confidence one step further, a data catalog integrated with data governance empowers an organization with quick and efficient insight discovery, so data users spend less time searching for the trusted data they need to feed into AI.

AI is hard, and it will be even harder if the technology is not built on delivering the foundational analytics capabilities such as enterprise-wide data schema that supports the full integration of all data assets, regardless of size or location, with governance. Qlik’s unique Associative technology paired with Qlik Data Catalyst enables Qlik Cognitive Engine to learn from all enterprise data generating trusted and governed insights.

@elif_tutuk highlights how Qlik is bridging the gap between data and artificial intelligence